News on China's scientific and technological development.

Yes it is Here is the news pick up by NYT and why is this invention important
https://www.nytimes.com/2019/07/31/science/bikes-robot-autonomous.html
And Now, a Bicycle Built for None
It’s not the first self-driving bike. But equipped with an A.I. chip, it may be the nearest to thinking for itself.



Image
A frame taken from a video by researchers in China shows a self-driving bicycle, whose neuromorphic computer chip helps it understand certain commands.CreditCreditPei et al., Nature

By Cade Metz

• July 31, 2019

As corporate giants like Ford, G.M. and Waymo struggle to get their self-driving cars on the road, a team of researchers in China is rethinking autonomous transportation using a souped-up bicycle.

This bike can roll over a bump on its own, staying perfectly upright. When the man walking just behind it says “left,” it turns left, angling back in the direction it came.

It also has eyes: It can follow someone jogging several yards ahead, turning each time the person turns. And if it encounters an obstacle, it can swerve to the side, keeping its balance and continuing its pursuit.

It is not the first-ever autonomous bicycle (Cornell University has a project underway) or, probably, the future of transportation, although it could find a niche in a future world swarming with package-delivery vehicles, drones and robots. (There are even weirder ideas out there.) Nonetheless, the Chinese researchers who built the bike believe it demonstrates the future of computer hardware. It navigates the world with help from what is called a neuromorphic chip, modeled after the human brain.

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In a paper published on Wednesday in Nature, the researchers described how such a chip could help machines respond to voice commands, recognize the surrounding world, avoid obstacles and maintain balance. The researchers also provided a video showing these skills at work on a motorized bicycle.

open a door or toss a Ping-Pong ball into a plastic bin, but the training takes hours to days of trial and error. Even then, the skills are viable only in very particular situations. With help from neuromorphic chips and other new processors, machines could learn more complex tasks more efficiently, and be more adaptable in executing them.

“That is where we see the big promise,” said Mike Davies, who oversees Intel’s efforts to build neuromorphic chips.

Over the past decade, the development of artificial intelligence has accelerated thanks to what are called neural networks: complex mathematical systems that can learn tasks by analyzing vast amounts of data. By metabolizing thousands of cat photos, for instance, a neural network can learn to recognize a cat.

This is the technology that recognizes faces in the photos you post to Facebook, identifies the commands you bark into your smartphone and translates between languages on internet services like Microsoft Skype. It is also hastening the advance of autonomous robots, including self-driving cars. But it faces significant limitations.

A neural network doesn’t really learn on the fly. Engineers train a neural network for a particular task before sending it out into the real world, and it can’t learn without enormous numbers of examples. OpenAI, a San Francisco artificial intelligence lab, recently built a system that could beat the world’s best players at a complex video game called Dota 2. But the system first spent months playing the game against itself, burning through millions of dollars in computing power.

Researchers aim to build systems that can learn skills in a manner similar to the way people do. And that could require new kinds of computer hardware. Dozens of companies and academic labs are now developing chips specifically for training and operating A.I. systems. The most ambitious projects are the neuromorphic processors, including the Tianjic chip under development at Tsinghua University in China.

Such chips are designed to imitate the network of neurons in the brain, not unlike a neural network but with even greater fidelity, at least in theory.

Neuromorphic chips typically include hundreds of thousands of faux neurons, and rather than just processing 1s and 0s, these neurons operate by trading tiny bursts of electrical signals, “firing” or “spiking” only when input signals reach critical thresholds, as biological neurons do.

the promise du jour. Maybe start with helping it learn to ride a bike.

 

Here is the principle behind this breakthrough via JSCH

Nature Cover Story | Chinese Team’s ‘Tianjic Chip’ Bridges Machine Learning and Neuroscience in Pursuit of AGI
Synced
Aug 1 · 4 min read



Many AI experts believe humanlike artificial general intelligence (AGI) is but a far-fetched dream, while others find their inspiration in the quest for AGI. Speaking at last November’s AI Frontiers Conference, OpenAI Founder and Research Director Ilya Sutskever said “We (OpenAI) have reviewed progress in the field over the past few years. Our conclusion is near-term AGI should be taken as a serious possibility.”

Today, respected scientific journal Nature boosted the case for AGI with a cover story on a new research paper, Towards artificial general intelligence with hybrid Tianjic chip architecture, which aims to stimulate AGI development by adopting generalized hardware platforms.



Typically, researchers have taken one of two paths in pursuit of AGI — proceeding either via computer science or via neuroscience. Each approach however requires its own unique and incompatible platforms, and this has stalled overarching AGI research and development. With an eye on closing that gap, researchers from Tsinghua University, Beijing Lynxi Technology, Beijing Normal University, Singapore Polytechnic University and University of California Santa Barbara have introduced the Tianjic chip. The revolutionary chip can adopt various core architectures, reconfigurable building blocks and so on, to accommodate both computer-science-based machine-learning algorithms and neuroscience-oriented schemes such as brain-inspired circuits.



Tianjic design

A key innovation from the research team is Tianjic’s unified function core (FCore) which combines essential building blocks for both artificial neural networks and biologically networks — axon, synapse, dendrite and soma blocks. The 28-nm chip consists of 156 FCores, containing approximately 40,000 neurons and 10 million synapses in an area of 3.8×3.8 mm2.

 

(cont)
Tianjic delivers an internal memory bandwidth of more than 610 gigabytes (GB) per second, and a peak performance of 1.28 tera operations per second (TOPS) per watt for running artificial neural networks. In the biologically-inspired spiking neural network mode, Tianjic achieves a peak performance of about 650 giga synaptic operations per second (GSOPS) per watt. The research team also showcased the superior performance of Tianjic compared to GPU, where the new chip achieves 1.6–100 times better throughput and 12–10000 times better power efficiency.


Chip evaluation and modeling

The research team designed a self-driving bicycle experiment to evaluate the chip’s capability for integrating multimodal information and making prompt decisions. Equipped with the Tianjic chip and IMU sensor, a camera, steering motor, driving motor, speed motor and battery, the bicycle was tasked with performing functions such as real-time object detection, tracking, voice-command recognition, riding over a speed bump, obstacle avoidance, balance control and decision making.



The research team developed a variety of neural networks (CNN, CANN, SNN and MLP networks) to enable each task. The models were pretrained and programmed onto the Tianjic chip, which can process the models in parallel and enable seamless on-chip communication across different models.

In experiments, the Tianjic-powered bicycle smoothly performed all assigned tasks, signaling a huge leap towards the acceleration of AGI development.

The research team also noted that “high spatiotemporal complexity can be generated by randomly introducing new variables into the environment in real time, such as different road conditions, noises, weather factors, multiple languages, more people and so on. By exploring solutions that allow adaptation to these environmental changes, issues critical to AGI — such as generalization, robustness and autonomous learning — can be examined.”



The research team told Chinese media they expect the Tianjic chip to be deployed in autonomous vehicles and smart robots. They have already started research on the next-generation chips and expect to close the R&D stage early next year.

Further information can be found in the paper Towards artificial general intelligence with hybrid Tianjic chip architecture.

Journalist: Tony Peng & Fangyu Cai | Editor: Michael Sarazen

 

but but... Chinese can only steal intellectual properties, not inventing them!!!

 

now noticed the tweet https://twitter.com/PDChina/status/1157890681756692480
People's Daily, China
@PDChina
·
2h
China's first #5G smartphones will start selling on August 5, announced ZTE, China's telecom giant. The ZTE Axon 10 Pro 5G pre-sale was launched on several Chinese e-commerce platforms for 4,999 yuan (about $727) on July 23.

 

It is a bit more of advertisement by using the word "neuroscience" in AI than pure technical meaning. A common trend these days. Similar to Samsung calling their "LED backlighted LCD panel" a LED screen.

I understand the very different nature of AI from traditional digital computing, BUT whatever fancy names (neuroscience) all AI today are STILL based on and running on CPUs no different from decades ago, binary based adding/subtracting. "Neuroscience" in AI introduce the idea of mimicking neuro-nerves network that is different from traditional sequential executing computer, but every neuro-node is still a typical CPU executing sequential instructions. It is very close to distributed computing without a central-coordinating node, which in my field is still a typical computer.

 

https://www.asiatimes.com/2019/08/article/chinese-buses-are-magic-in-santiago/

 

yeapp, Chinese definitely has stolen this technology from the US which doesn't have one ... like 5G